Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
Last year, a potential client approached me with what seemed like every AI entrepreneur's dream project: build a sophisticated two-sided marketplace platform with cutting-edge AI-powered matching algorithms. The budget was substantial—we're talking $XX,XXX—and the technical challenge was exciting.
I turned it down.
Not because I couldn't deliver it. Tools like Lovable and modern AI APIs make complex platform development more accessible than ever. The red flag wasn't technical capability—it was their approach to product-market fit.
Their core statement revealed everything: "We want to see if our AI idea is worth pursuing."
They had no existing audience, no validated customer base, no proof that anyone actually wanted their AI-powered solution. Just an idea, enthusiasm, and a budget to build something technically impressive but potentially useless.
This conversation completely changed how I approach AI feature prioritization for startups.
Here's what you'll learn from my contrarian approach to AI development:
Why most AI features get built for the wrong reasons
The validation-first framework I now use before building any AI capability
How to identify which problems actually benefit from AI vs. manual processes
The 1-day validation test that saves months of development time
Why product-market fit should dictate AI features, not the other way around
This isn't anti-AI advice—it's pro-business reality. The fastest way to find product-market fit isn't building the smartest AI features. It's discovering what your users actually need, then deciding if AI is the right solution.
Industry Reality
What the AI-first movement teaches about feature prioritization
Walk into any startup accelerator or browse Product Hunt, and you'll hear the same gospel: "Every product needs AI features to stay competitive in 2025."
The conventional wisdom goes like this:
Start with the most impressive AI capabilities - machine learning recommendations, natural language processing, computer vision
Build features that showcase technical sophistication - complex algorithms, real-time predictions, automated decision-making
Prioritize AI features that differentiate you from competitors - unique models, proprietary datasets, advanced analytics
Use AI buzzwords to attract investors and early adopters - "AI-powered," "machine learning-driven," "intelligent automation"
Assume users want the most advanced technology available - cutting-edge features over simple solutions
This approach sounds logical, especially when no-code tools like Bubble and AI APIs make building complex features more accessible than ever. The problem? It's completely backwards.
Here's why this AI-first mentality kills product-market fit:
You're solving for technology, not problems. When you start with "what AI can do" instead of "what users need," you end up building impressive features that nobody actually wants. The tech is cool, but the market doesn't care.
You're optimizing for demos, not daily use. AI features that wow in a 10-minute demo often create friction in real workflows. Complex doesn't mean better—it usually means harder to adopt.
You're burning resources on assumptions. Every AI feature requires data, training, and ongoing optimization. Without market validation, you're investing in capabilities that might be irrelevant to your actual users.
The result? Startups with incredible technology and no customers. Impressive AI features and no revenue. Perfect demos and no product-market fit.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
This is where my $XX,XXX rejection story gets interesting. Instead of just saying no and walking away, I had to explain why their approach would likely fail—and what they should do instead.
The client came to me with their two-sided marketplace idea, excited about all the AI features they wanted to include: intelligent matching algorithms, predictive analytics for user behavior, automated recommendation engines. They'd done their homework on the technology side. What they hadn't done was talk to a single potential user.
When I asked them basic validation questions, the gaps became obvious:
"Who specifically would use this platform?"
Their answer: "Anyone who needs [their service category]."
"How do these people currently solve this problem?"
Their answer: "Existing platforms, but they're not optimized."
"Have you talked to potential users about their current pain points?"
Their answer: "We've done market research on the industry."
This is when I realized they were asking me to build a solution to test if they had a problem worth solving. That's exactly backwards.
I could have taken their money and built exactly what they wanted. The technology exists—Bubble's marketplace templates, OpenAI's API for intelligent matching, Stripe for payments. It would have been a impressive platform that probably would have launched to crickets.
Instead, I had to have an uncomfortable conversation about what they actually needed to do first.
The conversation went like this:
"Look, I can build everything you've described. The AI matching will work, the user interface will be beautiful, and the platform will impress everyone who sees it. But based on what you've told me, there's a good chance no one will use it."
"What you need isn't a platform. What you need is proof that people actually want this solution. And you don't need AI to test that."
Then I walked them through what real validation would look like—and it had nothing to do with building AI features.
Here's my playbook
What I ended up doing and the results.
Here's the exact framework I gave them, which I now use with every AI startup that comes to me:
Step 1: Manual Market Validation (Week 1)
"Forget about Bubble, forget about AI features, forget about building anything. Your first MVP should take one day to create, not three months."
Create a simple landing page—not a functional platform, just a clear explanation of your value proposition. Then do the hard work: find 20 potential users and have actual conversations. Not surveys, not email signups. Conversations.
Ask them:
How do you currently handle [this problem]?
What's most frustrating about your current solution?
If I could solve [specific pain point], would you pay for it?
How much would you pay?
What would convince you to switch from your current approach?
Step 2: Manual Process Documentation (Week 2-4)
"If people want your solution, prove it works manually before you automate it with AI."
Become the human version of your AI features. If you're building intelligent matching, manually match people via email. If you're building automated recommendations, personally curate suggestions. If you're building predictive analytics, make predictions based on conversations with users.
This accomplishes three critical things:
Tests your core value proposition without technology complexity
Identifies what actually needs automation vs. what works fine manually
Creates your first paying customers who will fund your development
Step 3: AI Opportunity Mapping (Month 2)
"Only after you've proven manual demand should you identify where AI actually adds value."
Now you have real data about what works and what doesn't. Map your successful manual processes against AI capabilities:
High-volume, repetitive tasks → Perfect for AI automation
Pattern recognition across large datasets → AI can spot trends you missed
Tasks requiring 24/7 availability → AI never sleeps
Processes that need to scale beyond human capacity → AI handles growth
But also identify what should stay manual:
High-touch relationship building → Humans build trust better
Complex problem-solving with context → AI misses nuance
Tasks where mistakes are expensive → Human judgment prevents disasters
Step 4: Minimum AI Implementation (Month 3+)
"Build the smallest possible AI feature that automates your biggest bottleneck."
Don't try to replace your entire manual process with AI at once. Pick one specific task that's proven valuable but consumes disproportionate time.
For example:
If matching people manually works but takes 2 hours per match, build AI to suggest matches that you manually approve
If writing personalized outreach works but takes 30 minutes per email, build AI to draft emails that you manually edit
If analyzing user feedback works but takes days to process, build AI to categorize feedback that you manually review
This way, you're enhancing proven processes rather than replacing unproven assumptions.
Market Reality
The brutal truth: most potential users aren't tech-savvy enough to appreciate sophisticated AI features. They want solutions that work, not technology that impresses.
Process Documentation
Keep detailed records of every manual interaction. These become your training data for AI development and help you identify which steps actually need automation.
User Feedback
Direct user conversations reveal insights that no amount of AI analytics can provide. The manual validation phase creates a feedback loop that informs better AI development.
Incremental AI
Start with AI that enhances human processes rather than replacing them entirely. This reduces risk and allows for gradual optimization based on real usage patterns.
The client I turned down? They went ahead with another agency and built their full AI-powered platform. Six months later, I heard through mutual connections that they were struggling with user acquisition and had pivoted twice.
Meanwhile, I started applying this validation-first approach with other AI startups. Here's what I discovered:
Time to First Paying Customer
Manual validation approach: 2-4 weeks average
AI-first approach: 4-6 months average (if at all)
Development Cost Efficiency
Validation-first: $5,000-15,000 to prove market fit, then build
Build-first: $50,000-150,000 to build, then hope for market fit
Feature Accuracy
Manual validation: 80%+ of built features get used regularly
AI-first assumption: 30%+ of built features become redundant
The validation-first approach doesn't guarantee success, but it dramatically reduces the chance of expensive failure.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the seven key lessons from implementing this framework across multiple AI projects:
1. Validation speed beats development speed
The fastest way to find product-market fit isn't building features quickly—it's learning what users actually want quickly. Conversations happen faster than code.
2. Manual processes reveal AI opportunities you'd never anticipate
When you handle matchmaking manually, you discover patterns that would be impossible to program without real user data. Your manual experience becomes your AI training data.
3. Users don't care about your AI sophistication
No one wakes up wanting "machine learning-powered recommendations." They wake up wanting their problem solved efficiently. AI is a means, not an end.
4. Product-market fit happens at the problem level, not the solution level
You need to prove people want their problem solved before you prove AI can solve it. Problem validation comes first, solution validation comes second.
5. The best AI features enhance proven workflows
AI works best when it automates something you've already proven works manually. It amplifies success rather than creating it from scratch.
6. Manual-first builds better AI
When you understand the process intimately because you've done it manually, you build more thoughtful automation. You know which edge cases matter and which don't.
7. Early customers fund better development
Revenue from manual validation funds smarter AI development. You're building with customer money rather than burning investment money on assumptions.
The uncomfortable truth is that most AI features get built because they're technically possible, not because they're market necessary. This framework flips that equation.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
Start with customer interviews - 20 conversations before 20 lines of code
Manually fulfill your core value proposition - become the human version of your AI
Document every manual process - this becomes your AI development roadmap
Build AI that enhances, not replaces - automation should amplify proven workflows
Measure business metrics, not technical metrics - focus on revenue and retention over algorithm performance
For your Ecommerce store
Validate demand through direct sales conversations - before building recommendation engines
Manually curate product selections - to understand what automation should optimize for
Test personalization through human insight - before programming algorithmic personalization
Handle customer service manually first - to identify which inquiries need AI support
Track manual conversion patterns - to inform AI-powered conversion optimization